The transition of Large Language Models (LLMs) from experimental notebooks to production-grade applications requires more than just a well-crafted prompt. As enterprises integrate Generative AI into their core workflows, the need for stability, scalability, and reproducibility becomes paramount. This is where LLMOps—the intersection of DevOps, Data Engineering, and Machine Learning—enters the frame.

Building a CI/CD pipeline for LLM-based applications on Google Cloud Platform (GCP) presents unique challenges. Unlike traditional software, LLM outputs are non-deterministic, making testing complex.…